Label Smoothing for Enhanced Text Sentiment Classification

Label smoothing is a widely used technique in various domains, such as image classification and speech recognition, known for effectively combating model overfitting. However, there is few research on its application to text sentiment classification. To fill in the gap, this study investigates the implementation of label smoothing for sentiment classification by utilizing different levels of smoothing. The primary objective is to enhance sentiment classification accuracy by transforming discrete labels into smoothed label distributions. Through extensive experiments, we demonstrate the superior performance of label smoothing in text sentiment classification tasks across eight diverse datasets and deep learning architectures: TextCNN, BERT, and RoBERTa, under two learning schemes: training from scratch and fine-tuning.